Artificial neural network (ANN) assisted prediction of transient NO <sub>x</sub> emissions from a high-speed direct injection (HSDI) diesel engine
Xiaohang Fang, Fengyu Zhong, Nick Papaioannou, Martin Davy, Felix Leach
Abstract
The understanding and prediction of NO x emissions formation mechanisms during engine transients are critical to the monitoring of real driving emissions. While many studies focus on the engine out NO x formation and treatment, few studies consider cyclic transient NO x emissions due to the low time resolution of conventional emission analysers. Increased computational power and substantial quantities of accessible engine testing data have made ANN a suitable tool for the prediction of transient NO x emissions. In this study, the transient predictive ability of artificial neural networks where a large number of engine testing data are available has been studied extensively. Significantly, the proposed transient model is trained from steady-state engine testing data. The trained data with 14 input features are provided with transient signals which are available from most engine testing facilities. With the help of a state-of-art high-speed NO x analyser, the predicted transient NO x emissions are compared with crank-angle resolved NO x measurements taken from a high-speed light duty diesel engine at test conditions both with and without EGR. The results show that the ANN model is capable of predicting transient NO x emissions without training from crank-angle resolved data. Significant differences are captured between the predicted transient and the slow-response NO x emissions (which are consistent with the cycle-resolved transient emissions measurements). A particular strength is found for increasing load steps where the instantaneous NO x emissions predicted by the ANN model are well matched to the fast-NO x analyser measurements. The results of this work indicate that ANN modelling could strongly contribute to the understanding of real driving emissions.